I run Qwen 3.6 27B quantized down to Q4_K_M or even a variant Q5_K_S on my 8gb VRAM entry level AMD GPU, with support of my CPU and 32gb system RAM. Yes, I also limit the Context Length heavily to something like 18k. It’s slow. But the point is, you don’t need necessarily 16gb VRAM.
But it’s better to use faster models for this type of hardware anyway. The MoE type of models (such as 26B A4B, or 35B A3B) are vastly, vastly faster for normal usage, but they are a bit worse in some cases. Also Googles QAT trained model versions also have less RAM requirements without losing much quality.
I’m just saying that, so others are not discouraged too much. It’s not the same experience as the full version off course, but you can use them with some tricks on weak hardware too.
I’m fairly optimistic that people will figure out how to optimize the models a lot further going forward. One obvious path is to try and separate the reasoning network from the trivia that gets baked into the model, and some work is being done in this area. If you could have a context free reasoning engine and then feed the facts it needs to know on the fly based on the context you’re running it in, then you could likely have a much smaller model that’s very capable.
You don’t want to run these models in RAM. I started using them on an RTX 3060-12Gb VRAM, and quickly added another, for 24Gb RAM. I also have 64 Gb RAM. Now it works well. Not lightning fast, but quite useable. VRAM is the key.
what kinda hardware do you need to “run this on your desktop”?
You need a GPU with around 16gb vram at a minimum to run qunatized version.
I run Qwen 3.6 27B quantized down to Q4_K_M or even a variant Q5_K_S on my 8gb VRAM entry level AMD GPU, with support of my CPU and 32gb system RAM. Yes, I also limit the Context Length heavily to something like 18k. It’s slow. But the point is, you don’t need necessarily 16gb VRAM.
But it’s better to use faster models for this type of hardware anyway. The MoE type of models (such as 26B A4B, or 35B A3B) are vastly, vastly faster for normal usage, but they are a bit worse in some cases. Also Googles QAT trained model versions also have less RAM requirements without losing much quality.
I’m just saying that, so others are not discouraged too much. It’s not the same experience as the full version off course, but you can use them with some tricks on weak hardware too.
I’m fairly optimistic that people will figure out how to optimize the models a lot further going forward. One obvious path is to try and separate the reasoning network from the trivia that gets baked into the model, and some work is being done in this area. If you could have a context free reasoning engine and then feed the facts it needs to know on the fly based on the context you’re running it in, then you could likely have a much smaller model that’s very capable.
You can run it on systems with as little as 32 GBs of RAM.
You don’t want to run these models in RAM. I started using them on an RTX 3060-12Gb VRAM, and quickly added another, for 24Gb RAM. I also have 64 Gb RAM. Now it works well. Not lightning fast, but quite useable. VRAM is the key.
Sure, but with a model of that size you are unloading layers in any case. I’m just saying you need 32 GBs of RAM at the minimum to be able to run it.